A method includes receiving call records from a control plane, each call record including a cell list identifying the server cell for the UE call session at the time the call record was generated and an ordered set of neighbor cells, ordered based on a characteristic of signals from the neighbor cells. Call records having truth data are selected, wherein the truth data includes geolocation (GL) data reported to be a GL associated with the call record. GL data of the selected call records is stored in association with the cell list for the selected call records. A centroid is determined as a function of the GL data associated with each of the selected call records that includes the associated cell list. The centroids are stored in association with the corresponding cell list, and can be retrieved as a prediction for a GL based on submission of a cell list.

Location of objects within an identified subspace defined within a predefined master space. The system includes an RF beacon associated with an object, the RF beacon transmitting signals to one or more of RF tags transmitting to an object location system a tag data package including an identification of the signals received by the RF tags from the RF beacon. The system includes a gateway receiving and extracting, from the transmitted tag data package, the identification of signals. The system further includes the location engine accessing an ML model trained by performing a survey of RF signals received from a plurality of RF signal sources during a prior RF master space survey operation and determining a subspace identifier corresponding to a predicted subspace location for the object by comparing the identification of signals included in the received tag data package to a model plurality of signals accessed from the model.

A method and a location service component for providing an estimate of a location related to a device are disclosed. The location service component receives a request from a client for the estimate of the location and estimates the location based on a predictive model to obtain an initial set of data items. Each data item includes a respective estimate of the location and a set of identities representing one or more further devices in a neighborhood of the device. The location service component obtains a respective location for each of the further devices based on the set of identities. The location service component removes data items that are inconsistent with the respective location for the further devices to obtain a pruned set of data items. The location service component sends, estimates of the pruned set of data items to the client. A corresponding computer program and a computer program carrier are also disclosed.

A method and system is disclosed for determining a probability that an encountered platform was of a specific type given that a plurality of emitters exist on the platform and each emitter has a computed probability that it is of each of a set of types. A preprocessing stage operates on a description of the environment and determines the probability of a set of events that are independent of any observation. A runtime processing stage uses the terms computed in the preprocessing stage along with data assembled from a set of observations to determine the conditional probability that a particular platform type was the type encountered.

It is provided a method for enabling determination of proximity of a mobile device (2a, 2b) to a beacon device (3a). The method is performed by a proximity determiner (1) and comprises the steps of: determining a set of beacon devices (3a-e) being detectable from the mobile device; obtaining beacon measurements of one or two beacon devices in each pair; generating a two-dimensional graph based on the beacon measurements; obtaining device measurements of signal strength of beacons from the beacon devices in the set of beacon devices; finding an optimum in a space defined by the two- dimensional graph; and determining the most probable position of the mobile device in the graph based on the optimum.

Systems and methods for locating tagged objects in remote regions are presented herein, in one embodiment, a method of locating tagged objects in remote regions includes creating a signal strength probability density map by. The method also includes transmitting first packets of data from at least one first tag to a plurality of stations and determining, by a plurality of stations, received signal strength indicator (RSSI) for received first packets of data. The method also includes transmitting, by the plurality of stations, the RSSI to an uplink node; and transmitting, by the uplink node, the RSSI to a database. The method further includes determining, by the database, the signal strength probability density map representative of probabilistic locations of the at least one first tag; and transmitting second packets of data from a second tag to the plurality of stations. Based on the signal strength probability density map and the second packets of data from the second tag, a location of the second tag is determined.

To estimate a positional relationship between devices having transmitted and received signals with higher accuracy.

A control device includes a control unit that compares reliability parameters that are indexes indicating a degree of whether or not a signal is appropriate as a processing target for estimating a positional relationship between each of the plurality of communication devices and the other communication device, calculated on the basis of the signals received from the other communication device by the communication device, and performs control for estimating the positional relationship on the basis of a signal transmitted and received between the communication device that has received a signal that is more appropriate as a processing target for estimating the positional relationship and the other communication device.

Various techniques are provided for counting and/or tracking objects within a field of view of an imaging system, while excluding certain objects from the results. A monitoring system may count or track people identified in captured images while utilizing an employee identification system including a wireless signal receiver to identify and remove the employees from the result. The system includes algorithms for separating employee counts from customer counts, thereby offering enhanced tracking analytics.

Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform object location estimation.

A method and system for generating a target map for training a neural network and obtaining a sound source map regardless of the maximum number of sound sources, having a short computation time for inference, high spatial resolution and high sound source accuracy. The method includes generating grids each having a spacing within a given range at positions where sound sources are present in order to form a sound source map, calculating a result value for each of coordinates of the grids so that the result value is a local maximum at the position of a sound source and the result value decreases depending on the distance from the sound source, arranging the result values at positions on matrices corresponding to the respective coordinates of the grids, and generating a target map having an image form by using the result values arranged in on the matrices.